Effect Estimates, Effect Sizes, Confidence Intervals, and P-values: A Framework for Reporting and Interpreting Biomedical Research.

The interpretation of biomedical research findings should extend beyond statistical significance. Effect estimates, effect sizes, confidence intervals, and P-values provide information regarding the magnitude, direction, precision, and statistical evidence of an observed finding. However, these concepts are frequently misunderstood or interpreted in isolation, potentially leading to inappropriate conclusions about their clinical importance. This article presents a framework comprising effect estimate, effect size, confidence interval, and P-value (E-E-C-P) to facilitate comprehensive interpretation of biomedical results. Using clinically relevant examples, the framework demonstrates how these measures address different but interconnected questions: what was observed, how large the effect was, how precise the estimate is, and how strong the statistical evidence is. To translate these concepts into an interactive learning environment, an R Shiny application (University of Auckland in Auckland, New Zealand), EffectSight, was developed as a pedagogical tool using a single illustrative example focused on Cohen's d. The application allows learners to modify group means, standard deviations, and sample sizes and observe how these changes influence the effect estimate, Cohen's d, confidence interval, and P-value, with support from graphical visualizations and the E-E-C-P analysis grid. The application was designed specifically to facilitate conceptual understanding rather than to serve as a comprehensive statistical analysis tool. The E-E-C-P framework emphasizes that statistical significance should be interpreted alongside the magnitude and precision of an observed effect rather than considered in isolation.